The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). Additionally, CIM serves as a media of communications between the governing body and its membership of IEEE/CIS. Authors are encouraged to submit papers on applications oriented developments, successful industrial implementations, design tools, technology reviews, computational intelligence education, and applied research.
Contributions should contain novel and previously unpublished material. The novelty will usually lie in original concepts, results, techniques, observations, hardware/software implementations, or applications, but may also provide syntheses or new insights into previously reported research. Surveys and expository submissions are also welcome. In general, material which has been previously copyrighted, published or accepted for publication will not be considered for publication; however, prior preliminary or abbreviated publication of the material shall not preclude publication in this journal.
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Call for Special Issues
- Nov 2017: Computational Intelligence in Aerospace Science and Engineering
- Feb 2018: Computational Intelligence for Mobile Network Optimization
- May 2018: Automated Design of Machine Learning and Search Algorithms
- Aug 2018: Computational Intelligence Techniques in Bioinformatics and Bioengineering
- Nov 2018: Computational Intelligence in Finance and Economics
- Feb 2019: Memorial Special Issue for Prof. Lotfi A. Zaheh
- May 2019: Affective Computing and Sentiment Analysis
- Aug 2019: Special Issue on Deep Reinforcement Learning and Games
- Nov 2019: Computational Intelligence for Internet of Things in the Big Data Era
Selected article from IEEE Computational Intelligence Magazine
Is Evolutionary Computation Evolving Fast Enough?
Evolutionary Computation (EC) has been an active research area for over 60 years, yet its commercial/home uptake has not been as prolific as we might have expected. In this paper we provide a brief history of EC, recognizing the significant contributions that have been made by its pioneers. We focus on two methodologies (Genetic Programming and Hyper-heuristics), which have been proposed as being suitable for automated software development, and question why they are not used more widely by those outside of the academic community. We suggest that different research strands need to be brought together into one framework before wider uptake is possible. We hope that this position paper will serve as a catalyst for automated software development that is used on a daily basis by both companies and home users.
IEEE Computational Intelligence Magazine, May 2018